Effect of photobleaching on calibration model development in biological Raman spectroscopy

Abstract

A major challenge in performing quantitative biological studies using Raman spectroscopy lies in overcoming the influence of the dominant sample fluorescence background. Moreover, the prediction accuracy of a calibration model can be severely compromised by the quenching of the endogenous fluorophores due to the introduction of spurious correlations between analyte concentrations and fluorescence levels. Apparently, functional models can be obtained from such correlated samples, which cannot be used successfully for prospective prediction. This work investigates the deleterious effects of photobleaching on prediction accuracy of implicit calibration algorithms, particularly for transcutaneous glucose detection using Raman spectroscopy. Using numerical simulations and experiments on physical tissue models, we show that the prospective prediction error can be substantially larger when the calibration model is developed on a photobleaching correlated dataset compared to an uncorrelated one. Furthermore, we demonstrate that the application of shifted subtracted Raman spectroscopy (SSRS) reduces the prediction errors obtained with photobleaching correlated calibration datasets compared to those obtained with uncorrelated ones.

Figure 1

(a) Raman spectra acquired from a human volunteer during an OGTT. (b) Normalized tissue autofluorescence decay (I_λ∕I_exc) obtained as a function of time during the OGTT performance (the measured data points are in circles and the dotted line is the best-fit double exponential curve).

Figure 2

Representative glucose concentration profiles taken from two human volunteers: profile 1 (top) and profile 2 (bottom).

Figure 3

A schematic diagram of the experimental setup. Raman spectra were obtained from tissue phantom solutions using an optical fiber probe, which included a laser light delivery fiber and ten collection fibers. The spectrograph was equipped with a micrometer, which was used to precisely tune the grating for implementing SSRS. F1: laser line filter. S: shutter. L1: focusing lens for optical fiber coupling. F2: Rayleigh rejection edge filter.

Figure 4

Bar plot of RMSEP obtained for calibration models developed on photobleaching correlated and uncorrelated datasets, respectively, as a function of increasing fluorescence-to-Raman ratio (i.e., decreasing Raman-to-noise ratio). Here, the SNR is held constant (100). Identical results are obtained by changing the SNR while holding the fluorescence-to-Raman ratio fixed.

Figure 5

RMSEP of simulations as a function of the correlation (R²) between glucose concentration and fluorescence intensity. The fluorescence-to-Raman ratio (F∕R) was varied from 10, 15 to 20, and 20 simulations were performed for each case.

Figure 6

Bar plot of RMSEP values obtained for glucose concentrations from simulations on photobleaching correlated (red) and uncorrelated (blue) datasets. The groups represent calibration and prediction performed using the following types of spectra: (from left to right) unprocessed, lower order polynomial subtracted, modified polynomial subtracted, minmax fit subtracted, and SSRS processed spectra, respectively.

Figure 7

(a) Representative Raman spectrum acquired from a tissue phantom. (b) SSRS spectrum obtained by subtracting two spectra, obtained at spectrograph grating positions 25 cm^–1 apart. (c) First derivative spectrum.